22BS

Technical details

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library(GeoPressureR)
library(leaflet)
library(leaflet.extras)
library(raster)
library(dplyr)
library(ggplot2)
library(kableExtra)
library(plotly)
library(GeoLocTools)
setupGeolocation()
knitr::opts_chunk$set(echo = FALSE)
load(paste0("../data/1_pressure/", params$gdl_id, "_pressure_prob.Rdata"))
load(paste0("../data/2_light/", params$gdl_id, "_light_prob.Rdata"))
load(paste0("../data/3_static/", params$gdl_id, "_static_prob.Rdata"))
load(paste0("../data/4_basic_graph/", params$gdl_id, "_basic_graph.Rdata"))

Settings used

All the results produced here are generated with (1) the raw geolocator data, (2) the labeled files of pressure and light and (3) the parameters listed below.

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kable(gpr) %>% scroll_box(width = "100%")

include	gdl_id	crop_start	crop_end	thr_dur	extent_N	extent_W	extent_S	extent_E	map_scale	map_max_sample	map_margin	prob_map_s	prob_map_thr	shift_k	calib_lon	calib_lat	calib_1_start	calib_1_end	calib_2_start	calib_2_end	calib_2_lon	calib_2_lat	prob_light_w	thr_prob_percentile	thr_gs	RingNo	scientific_name	common_name	mass	wing_span	Color
TRUE	22BS	1900-01-01	2100-01-01	12	50	20	-35	120	2	300	30	1	0.9	21600	110.83	48.57	2018-07-15	2018-08-19	NA	NA	NA	NA	0.1	0.9	120	NA	NA	Eurasian Nightjar	NA	NA	NA

Pressure timeserie

The labeling of pressure data is illustrated with this figure. The black dots indicates the pressure datapoint not considered in the matching. Each stationay period is illustrated by a different colored line.

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pressure_na <- pam$pressure %>%
  mutate(obs = ifelse(isoutliar | sta_id == 0, NA, obs))
p <- ggplot() +
  geom_line(data = pam$pressure, aes(x = date, y = obs), colour = "grey") +
  geom_point(data = subset(pam$pressure, isoutliar), aes(x = date, y = obs), colour = "black") +
  # geom_line(data = pressure_na, aes(x = date, y = obs, color = factor(sta_id)), size = 0.5) +
  geom_line(data = do.call("rbind", shortest_path_timeserie) %>% filter(sta_id>0), aes(x = date, y = pressure0, col = factor(sta_id))) +
  theme_bw() +
  scale_colour_manual(values = col) +
  scale_y_continuous(name = "Pressure(hPa)")

ggplotly(p, dynamicTicks = T) %>% layout(showlegend = F)

Light

See geolocator manual for more information on this figure

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raw_geolight <- pam$light %>%
  transmute(
    Date = date,
    Light = obs
  )
lightImage(tagdata = raw_geolight, offset = gpr$shift_k/60/60)
tsimagePoints(twl$twilight,
  offset = gpr$shift_k/60/60, pch = 16, cex = 1.2,
  col = ifelse(twl$deleted, "grey20", ifelse(twl$rise, "firebrick", "cornflowerblue"))
)
abline(v = gpr$calib_2_start, lty = 1, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_1_start, lty = 1, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_2_end, lty = 2, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_1_end, lty = 2, col = "firebrick", lwd = 1.5)

The histogram of zenith angle at the calibration site together with the fitted kernel density. Note that we use a large bandwidth of the kernel to account of the bias of the calibration site.

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hist(z, freq = F)
lines(fit_z, col = "red")

The probability map resulting from light data alone can be seen below.

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li_s <- list()
l <- leaflet(width = "100%") %>%
  addProviderTiles(providers$Stamen.TerrainBackground) %>%
  addFullscreenControl()
for (i_r in seq_len(length(light_prob))) {
  i_s <- metadata(light_prob[[i_r]])$sta_id
  info <- pam$sta[pam$sta$sta_id == i_s, ]
  info_str <- paste0(i_s, " | ", info$start, "->", info$end)
  li_s <- append(li_s, info_str)
  l <- l %>% addRasterImage(light_prob[[i_r]], opacity = 0.8, colors = "OrRd", group = info_str)
}
l %>%
  addCircles(lng = gpr$calib_lon, lat = gpr$calib_lat, color = "black", opacity = 1) %>%
  addLayersControl(
    overlayGroups = li_s,
    options = layersControlOptions(collapsed = FALSE)
  ) %>%
  hideGroup(tail(li_s, length(li_s) - 1))

Light vs Pressure

We can compare light and pressure location at long stationary stopover (>5 days). By assuming the best match of the pressure to be the truth, we can plot the histogram of the zenith angle and compare to the fit of kernel density at the calibration site.

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 raw_geolight <- pam$light %>%
    transmute(
      Date = date,
      Light = obs
    )
 dur <- unlist(lapply(pressure_prob, function(x) difftime(metadata(x)$temporal_extent[2],metadata(x)$temporal_extent[1], units = "days" )))
  long_id <- which(dur>5)

  par(mfrow = c(2, 3))
  for (i_s in long_id){
    twl_fl <- twl %>%
      filter(!deleted) %>%
      filter(twilight>shortest_path_timeserie[[i_s]]$date[1] & twilight<tail(shortest_path_timeserie[[i_s]]$date,1))
    sun <-  solar(twl_fl$twilight)
    z_i <- refracted(zenith(sun, shortest_path_timeserie[[i_s]]$lon[1], shortest_path_timeserie[[i_s]]$lat[1]))
    hist(z_i, freq = F, main = paste0("sta_id=",i_s, " | ",nrow(twl_fl),"twls"))
    lines(fit_z, col = "red")
    xlab("Zenith angle")
  }

Similarly, we can plot the line of sunrise/sunset at the best match of pressure (yellow line) and compare to the raw and labeled light data.

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  lightImage(
    tagdata = raw_geolight,
    offset = gpr$shift_k / 60 / 60
  )
  tsimagePoints(twl$twilight,
                offset = gpr$shift_k / 60 / 60, pch = 16, cex = 1.2,
                col = ifelse(twl$deleted, "grey20", ifelse(twl$rise, "firebrick", "cornflowerblue"))
  )
  for (ts in shortest_path_timeserie){
    twl_fl <- twl %>%
      filter(twilight>ts$date[1] & twilight<tail(ts$date,1))
    tsimageDeploymentLines(twl_fl$twilight,
                           lon = ts$lon[1], ts$lat[1],
                           offset = gpr$shift_k / 60 / 60, lwd = 3,col = adjustcolor("orange", alpha.f = 0.5))
  }

GeoPressureViz

To visualize the path on GeoPressureViz, you will need to also load the pressure and light probability map and align them first with the code below.

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sta_marginal <- unlist(lapply(static_prob_marginal, function(x) raster::metadata(x)$sta_id))
sta_pres <- unlist(lapply(pressure_prob, function(x) raster::metadata(x)$sta_id))
sta_light <- unlist(lapply(light_prob, function(x) raster::metadata(x)$sta_id))
pressure_prob <- pressure_prob[sta_pres %in% sta_marginal]
light_prob <- light_prob[sta_light %in% sta_marginal]

The code below will open with the shortest path computed with the graph approach. You can change it to

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geopressureviz <- list(
  pam_data = pam,
  static_prob = static_prob,
  static_prob_marginal = static_prob_marginal,
  pressure_prob = pressure_prob,
  light_prob = light_prob,
  pressure_timeserie = shortest_path_timeserie
)
save(geopressureviz, file = "~/geopressureviz.RData")

shiny::runApp(system.file("geopressureviz", package = "GeoPressureR"),
  launch.browser = getOption("browser")
)

Stationay period information

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pam$sta %>% kable()

start	end	sta_id
2018-07-15 00:00:00	2018-08-19 16:10:00	1
2018-08-19 19:05:00	2018-08-19 19:55:00	2
2018-08-19 20:25:00	2018-08-20 14:35:00	3
2018-08-20 16:15:00	2018-08-22 12:25:00	4
2018-08-22 12:45:00	2018-08-27 13:25:00	5
2018-08-27 20:25:00	2018-08-28 12:15:00	6
2018-08-28 12:35:00	2018-08-29 12:10:00	7
2018-08-29 20:30:00	2018-08-30 14:45:00	8
2018-08-30 14:50:00	2018-08-31 13:25:00	9
2018-08-31 15:50:00	2018-09-01 20:30:00	10
2018-09-01 21:35:00	2018-09-02 15:20:00	11
2018-09-02 18:55:00	2018-09-03 17:35:00	12
2018-09-03 17:45:00	2018-09-05 13:35:00	13
2018-09-05 22:30:00	2018-09-06 13:40:00	14
2018-09-06 22:40:00	2018-09-07 13:55:00	15
2018-09-07 23:25:00	2018-09-08 13:30:00	16
2018-09-08 22:05:00	2018-09-09 13:40:00	17
2018-09-09 23:45:00	2018-09-10 14:50:00	18
2018-09-10 22:30:00	2018-09-11 13:45:00	19
2018-09-11 14:20:00	2018-09-14 23:35:00	20
2018-09-15 00:10:00	2018-09-15 13:35:00	21
2018-09-15 13:50:00	2018-09-20 16:45:00	22
2018-09-20 16:55:00	2018-09-21 13:30:00	23
2018-09-21 14:40:00	2018-09-21 23:35:00	24
2018-09-22 00:05:00	2018-09-23 23:15:00	25
2018-09-24 00:05:00	2018-09-27 18:15:00	26
2018-09-27 23:35:00	2018-09-28 13:45:00	27
2018-09-29 00:05:00	2018-09-29 13:50:00	28
2018-09-29 23:50:00	2018-09-30 14:10:00	29
2018-10-01 00:55:00	2018-10-01 14:35:00	30
2018-10-02 00:15:00	2018-10-02 14:10:00	31
2018-10-03 01:10:00	2018-10-03 16:30:00	32
2018-10-03 18:05:00	2018-10-04 14:05:00	33
2018-10-04 22:45:00	2018-10-05 14:15:00	34
2018-10-06 01:25:00	2018-10-06 14:45:00	35
2018-10-06 21:45:00	2018-10-07 15:10:00	36
2018-10-08 01:50:00	2018-10-08 15:30:00	37
2018-10-09 01:55:00	2018-10-09 15:40:00	38
2018-10-10 01:40:00	2018-10-16 23:20:00	39
2018-10-17 00:10:00	2018-10-22 14:55:00	40
2018-10-22 20:00:00	2018-10-23 14:55:00	41
2018-10-23 15:25:00	2018-10-24 00:55:00	42
2018-10-24 02:15:00	2018-10-24 14:55:00	43
2018-10-24 17:25:00	2018-10-25 00:35:00	44
2018-10-25 02:20:00	2018-10-25 15:00:00	45
2018-10-25 15:15:00	2018-10-26 01:15:00	46
2018-10-26 02:25:00	2018-10-26 21:05:00	47
2018-10-26 22:10:00	2018-10-27 15:00:00	48
2018-10-27 15:20:00	2018-10-28 01:40:00	49
2018-10-28 02:20:00	2018-10-28 15:00:00	50
2018-10-28 18:30:00	2018-10-31 16:05:00	51
2018-11-01 01:50:00	2018-11-01 17:45:00	52
2018-11-01 20:35:00	2018-11-02 17:50:00	53
2018-11-03 01:10:00	2018-11-05 21:35:00	54
2018-11-05 21:40:00	2018-11-06 22:45:00	55
2018-11-06 22:55:00	2018-11-07 18:15:00	56
2018-11-07 18:45:00	2018-11-08 20:30:00	57
2018-11-08 20:40:00	2018-11-09 15:30:00	58
2018-11-09 15:50:00	2018-11-10 02:20:00	59
2018-11-10 02:45:00	2018-11-10 15:30:00	60
2018-11-10 16:05:00	2018-11-10 23:45:00	61
2018-11-11 00:30:00	2018-11-17 15:35:00	62
2018-11-17 17:05:00	2018-11-18 01:30:00	63
2018-11-18 02:35:00	2018-11-18 15:40:00	64
2018-11-18 15:55:00	2018-11-19 02:20:00	65
2018-11-19 02:30:00	2018-11-19 16:30:00	66
2018-11-20 02:25:00	2018-11-20 17:25:00	67
2018-11-20 23:00:00	2018-11-21 22:15:00	68
2018-11-22 02:25:00	2018-11-22 18:05:00	69
2018-11-23 02:05:00	2018-11-23 18:45:00	70
2018-11-24 01:40:00	2018-11-24 16:15:00	71
2018-11-24 21:35:00	2018-11-25 18:25:00	72
2018-11-25 19:25:00	2018-11-26 20:15:00	73
2018-11-26 20:40:00	2018-11-27 02:05:00	74
2018-11-27 02:10:00	2019-03-14 20:20:00	75
2019-03-15 03:15:00	2019-03-15 22:35:00	76
2019-03-16 00:45:00	2019-03-16 16:10:00	77
2019-03-16 16:30:00	2019-03-16 23:30:00	78
2019-03-17 00:25:00	2019-03-17 16:15:00	79
2019-03-17 16:25:00	2019-03-18 03:40:00	80
2019-03-18 03:40:00	2019-03-18 16:10:00	81
2019-03-18 16:25:00	2019-03-19 16:15:00	82
2019-03-19 16:40:00	2019-03-20 16:15:00	83
2019-03-21 02:20:00	2019-03-21 15:40:00	84
2019-03-21 16:05:00	2019-03-22 15:55:00	85
2019-03-23 02:50:00	2019-03-23 15:55:00	86
2019-03-24 02:55:00	2019-03-24 15:55:00	87
2019-03-25 02:20:00	2019-03-25 16:00:00	88
2019-03-26 01:10:00	2019-03-26 18:35:00	89
2019-03-26 20:50:00	2019-03-27 16:50:00	90
2019-03-27 21:00:00	2019-03-28 20:50:00	91
2019-03-28 22:10:00	2019-03-30 19:15:00	92
2019-03-30 20:50:00	2019-03-31 12:40:00	93